Starting with Data
Objectives
- Describe what a data frame is.
- Load external data from a .csv file into a data frame.
- Summarize the contents of a data frame.
- Subset values from data frames.
- Describe the difference between a factor and a string.
- Convert between strings and factors.
- Reorder and rename factors.
- Change how character strings are handled in a data frame.
- Examine and change date formats.
Questions
- What is a data.frame?
- How can I read a complete csv file into R?
- How can I get basic summary information about my dataset?
- How can I change the way R treats strings in my dataset?
- Why would I want strings to be treated differently?
- How are dates represented in R and how can I change the format?
The main dataset used in this tutorial is the Danish kongerække digitized by BA students in the Digital Methods class on the basis of online resources in Danmarkshistorien and kongeraekke URL.
You can download the dataset here or use R to download it in your current folder:
# download the dataset
download.file("https://raw.githubusercontent.com/adivea/r-history/main/episodes/data/kings.csv",
destfile = "data/kings.csv")Please refrain from opening the file, we will load and open it first in R, to prevent your operating system from changing the file’s format
What are data frames and tibbles?
Data frames are the de facto data structure for tabular data
in R, and what we use for data processing, statistics, and
plotting.
A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Data frames are analogous to the more familiar spreadsheet in programs such as Excel, with one key difference. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.
Data frames can be created by hand, but most commonly they are
generated by the functions read_csv() or
read_table(); in other words, when importing spreadsheets
from your hard drive (or the web). We will now demonstrate how to import
tabular data using read_csv().
Presentation of the ‘kings’ data
We will be using a subset of the cleaned version of the dataset that
was produced through cleaning in OpenRefine
(data/kings.csv). In this dataset, the missing data is
encoded as “NULL”, each row holds information for a single interview
respondent, and the columns represent:
| column_name | description |
|---|---|
| Name | The unique name of Danish monarch |
| Start_year | starting year of the monarch’s rule |
| End_year | the last year of the monarch’s rule |
| Birth_year | the year the monarch was born |
| Death_year | the year the monarch died |
| House | the royal house the monarch belonged to |
| Dynasty | another term for the royal house |
Importing data
You are going to load the data in R’s memory using the function
read_csv() from the readr
package, which is part of the tidyverse;
learn more about the tidyverse collection
of packages here.
readr gets installed as part as the
tidyverse installation. When you load the
tidyverse
(library(tidyverse)), the core packages (the packages used
in most data analyses) get loaded, including
readr.
Before proceeding, however, this is a good opportunity to talk about
conflicts. Certain packages we load can end up introducing function
names that are already in use by pre-loaded R packages. For instance,
when we load the tidyverse package below, we will introduce two
conflicting functions: filter() and lag().
This happens because filter and lag are
already functions used by the stats package (already pre-loaded in R).
What will happen now is that if we, for example, call the
filter() function, R will use the
dplyr::filter() version and not the
stats::filter() one. This happens because, if conflicted,
by default R uses the function from the most recently loaded package.
Conflicted functions may cause you some trouble in the future, so it is
important that we are aware of them so that we can properly handle them,
if we want.
To do so, we just need the following functions from the conflicted package:
conflicted::conflict_scout(): Shows us any conflicted functions.
conflict_prefer("function", "package_prefered"): Allows us to choose the default function we want from now on.
It is also important to know that we can, at any time, just call the
function directly from the package we want, such as
stats::filter().
Before we can use the read_csv() functions, we need to
load the tidyverse packages.
Also, if you recall, the missing data is encoded as “NULL” in the
dataset. We’ll tell it to the function, so R will automatically convert
all the “NULL” entries in the dataset into NA.
In the above code, we notice the read_csv() function
takes folder and file names as inputs (e.g.,
"data/kings.csv"), each enclosed in quotations
("") and separated by a comma.
If you were to type in the code above, it is likely that the
read.csv() function would appear in the automatically
populated list of functions. This function is different from the
read_csv() function, as it is included in the “base”
packages that come pre-installed with R. Overall,
read.csv() behaves similar to read_csv(), with
a few notable differences. First, read.csv() coerces column
names with spaces and/or special characters to different names. Second,
read.csv() stores data as a data.frame, where
read_csv() stores data as a tibble. We prefer
tibbles because they have nice printing properties among other desirable
qualities. Read more about tibbles here.
The second statement in the code above creates a data frame but
doesn’t output any data because, as you might recall, assignments
(<-) don’t display anything. (Note, however, that
read_csv may show informational text about the data frame
that is created.) If we want to check that our data has been loaded, we
can see the contents of the data frame by typing its name:
kings in the console.
# A tibble: 54 × 11
Name House Start…¹ End_y…² Birth…³ Death…⁴ Gender Dynasty Source Birth…⁵ Death…⁶
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 Gorm den G… Gorm 936 958 908 958 M Jellin… https… 908 958
2 Harald 1. … Gorm NA NA 936 985 M Jellin… https… 936 987
3 Toke_Gorms… Gorm 985 986 NA 986 M Jellin… https… <NA> <NA>
4 Svend 1. T… Gorm NA NA NA 1014 M Jellin… https… 17/04/… 03/02/…
5 Harald 2. Gorm 1014 1018 963 NA M Jellin… https… 994 1018
6 Knud 1. de… Gorm 1018 1035 NA 1035 M Jellin… https… 995 12/11/…
7 Hardeknud Gorm 1035 1042 995 1042 M Jellin… https… 1018 08/06/…
8 Magnus den… Fair… 1042 1047 1018 1047 M Jellin… https… 1024 25/10/…
9 Svend 2. E… Estr… 1047 1074 1024 1076 M <NA> https… 1019 28/04/…
10 Harald 3. … Estr… 1074 1080 1019 1080 M Jellin… https… 1041 17/04/…
# … with 44 more rows, and abbreviated variable names ¹Start_year, ²End_year,
# ³Birth_year, ⁴Death_year, ⁵BirthDMY, ⁶DeathDMYNote
read_csv() assumes that fields are delimited by commas.
However, in several countries, the comma is used as a decimal separator
and the semicolon (;) is used as a field delimiter. If you want to read
in this type of files in R, you can use the read_csv
function. It behaves exactly like read_csv but uses
different parameters for the decimal and the field separators. If you
are working with another format, they can be both specified by the user.
Check out the help for read_csv() by typing
?read_csv to learn more. There is also the
read_tsv() for tab-separated data files, and
read_delim() allows you to specify more details about the
structure of your file.
Note that read_csv() actually loads the data as a
tibble. A tibble is an extension of R data frames used by
the tidyverse. When the data is read using
read_csv(), it is stored in an object of class
tbl_df, tbl, and data.frame. You
can see the class of an object with
[1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"
[1] "numeric"As a tibble, the type of data included in each column is
listed in an abbreviated fashion below the column names. For instance,
here Danish_kings is a column of characters
(<chr>), Start_year and
End_year are columns of floating point numbers (abbreviated
<dbl> for the word ‘double’).
Inspecting data frames
When calling a tbl_df object (like kings
here), there is already a lot of information about our data frame being
displayed such as the number of rows, the number of columns, the names
of the columns, and as we just saw the class of data stored in each
column. However, there are functions to extract this information from
data frames. Here is a non-exhaustive list of some of these functions.
Let’s try them out!
Size:
dim(kings)- returns a vector with the number of rows as the first element, and the number of columns as the second element (the dimensions of the object)nrow(kings)- returns the number of rowsncol(kings)- returns the number of columns
Content:
head(kings)- shows the first 6 rowstail(kings)- shows the last 6 rows
Names:
names(kings)- returns the column names (synonym ofcolnames()fordata.frameobjects)
Summary:
str(kings)- structure of the object and information about the class, length and content of each columnsummary(kings)- summary statistics for each columnglimpse(kings)- returns the number of columns and rows of the tibble, the names and class of each column, and previews as many values will fit on the screen. Unlike the other inspecting functions listed above,glimpse()is not a “base R” function so you need to have thedplyrortibblepackages loaded to be able to execute it.
Note: most of these functions are “generic.” They can be used on other types of objects besides data frames or tibbles.
Subsetting data frames
Our kings data frame has rows and columns (it has 2
dimensions). In practice, we may not need the entire data frame; for
instance, we may only be interested in a subset of the observations (the
rows) or a particular set of variables (the columns). If we want to
access some specific data from it, we need to specify the “coordinates”
(i.e., indices) we want from it. Row numbers come first, followed by
column numbers.
Tip
tibble with [ always results in a
tibble. However, note this is not true in general for data
frames, so be careful! Different ways of specifying these coordinates
can lead to results with different classes. This is covered in the
Software Carpentry lesson R for
Reproducible Scientific Analysis.
# A tibble: 1 × 1
Name
<chr>
1 Gorm den Gamle# A tibble: 1 × 1
Death_year
<dbl>
1 958 [1] "Gorm den Gamle" "Harald 1. Blåtand"
[3] "Toke_Gormsen" "Svend 1. Tveskæg"
[5] "Harald 2." "Knud 1. den Store"
[7] "Hardeknud" "Magnus den Gode"
[9] "Svend 2. Estridsen" "Harald 3. Hen"
[11] "Knud 2. den Hellige" "Oluf 1. Hunger"
[13] "Erik 1. Ejegod" "Niels"
[15] "Erik 2. Emune" "Erik 3. Lam"
[17] "Svend 3.; Knud 3.; Valdemar 1." "Valdemar 1. den Store"
[19] "Knud 4." "Valdemar 2. Sejr"
[21] "Erik 4. Plovpenning" "Abel"
[23] "Christoffer 1." "Erik 5. Klipping"
[25] "Erik 6. Menved" "Christoffer 2."
[27] "Valdemar 3." "Christoffer 2."
[29] "Valdemar 4. Atterdag" "Oluf 2."
[31] "Margrete 1." "Erik 7. af Pommern"
[33] "Christoffer 3. af Bayern" "Chrstian 1."
[35] "Hans" "Christian 2."
[37] "Frederik 1." "Christian 3."
[39] "Frederik 2." "Christian 4."
[41] "Frederik 3." "Christian 5."
[43] "Frederik 4." "Christian 6."
[45] "Frederik 5." "Christian 7."
[47] "Frederik 6." "Christian 8."
[49] "Frederik 7." "Christian 9."
[51] "Frederik 8." "Christian 10."
[53] "Frederik 9." "Margrete 2." # A tibble: 54 × 1
Name
<chr>
1 Gorm den Gamle
2 Harald 1. Blåtand
3 Toke_Gormsen
4 Svend 1. Tveskæg
5 Harald 2.
6 Knud 1. den Store
7 Hardeknud
8 Magnus den Gode
9 Svend 2. Estridsen
10 Harald 3. Hen
# … with 44 more rows# A tibble: 3 × 1
Gender
<chr>
1 M
2 M
3 M # A tibble: 1 × 11
Name House Start…¹ End_y…² Birth…³ Death…⁴ Gender Dynasty Source Birth…⁵ Death…⁶
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 Toke_Gormsen Gorm 985 986 NA 986 M Jellin… https… <NA> <NA>
# … with abbreviated variable names ¹Start_year, ²End_year, ³Birth_year, ⁴Death_year,
# ⁵BirthDMY, ⁶DeathDMY: is a special function that creates numeric vectors of
integers in increasing or decreasing order, test 1:10 and
10:1 for instance.
You can also exclude certain indices of a data frame using the
“-” sign:
# A tibble: 54 × 10
House Start_year End_year Birth_year Death_…¹ Gender Dynasty Source Birth…² Death…³
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 Gorm 936 958 908 958 M Jellin… https… 908 958
2 Gorm NA NA 936 985 M Jellin… https… 936 987
3 Gorm 985 986 NA 986 M Jellin… https… <NA> <NA>
4 Gorm NA NA NA 1014 M Jellin… https… 17/04/… 03/02/…
5 Gorm 1014 1018 963 NA M Jellin… https… 994 1018
6 Gorm 1018 1035 NA 1035 M Jellin… https… 995 12/11/…
7 Gorm 1035 1042 995 1042 M Jellin… https… 1018 08/06/…
8 Fairhair 1042 1047 1018 1047 M Jellin… https… 1024 25/10/…
9 Estridsen 1047 1074 1024 1076 M <NA> https… 1019 28/04/…
10 Estridsen 1074 1080 1019 1080 M Jellin… https… 1041 17/04/…
# … with 44 more rows, and abbreviated variable names ¹Death_year, ²BirthDMY, ³DeathDMY# A tibble: 6 × 11
Name House Start…¹ End_y…² Birth…³ Death…⁴ Gender Dynasty Source Birth…⁵ Death…⁶
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 Gorm den Ga… Gorm 936 958 908 958 M Jellin… https… 908 958
2 Harald 1. B… Gorm NA NA 936 985 M Jellin… https… 936 987
3 Toke_Gormsen Gorm 985 986 NA 986 M Jellin… https… <NA> <NA>
4 Svend 1. Tv… Gorm NA NA NA 1014 M Jellin… https… 17/04/… 03/02/…
5 Harald 2. Gorm 1014 1018 963 NA M Jellin… https… 994 1018
6 Knud 1. den… Gorm 1018 1035 NA 1035 M Jellin… https… 995 12/11/…
# … with abbreviated variable names ¹Start_year, ²End_year, ³Birth_year, ⁴Death_year,
# ⁵BirthDMY, ⁶DeathDMYtibbles can be subset by calling indices (as shown
previously), but also by calling their column names directly:
kings["Name"] # Result is a tibble
kings[, "Name"] # Result is a tibble
kings[["Name"]] # Result is a vector
kings$Name # Result is a vectorIn RStudio, you can use the autocompletion feature to get the full and correct names of the columns.
Exercise 1
Create a tibble (
kings_20) containing only the data in row 20 of thekingsdataset.Notice how
nrow()gave you the number of rows in the tibble?
- Use that number to pull out just that last row in the tibble.
- Compare that with what you see as the last row using
tail()to make sure it’s meeting expectations. - Pull out that last row using
nrow()instead of the row number. - Create a new tibble (
kings_last) from that last row.
Using the number of rows in the kings dataset that you found in question 2, extract the row that is in the middle of the dataset. Store the content of this middle row in an object named
kings_middle. (hint: This dataset has an odd number of rows, so finding the middle is a bit trickier than dividing n_rows by 2. Use the median( ) function and what you’ve learned about sequences in R to extract the middle row!Combine
nrow()with the-notation above to reproduce the behavior ofhead(kings), keeping just the first through 6th rows of the kings dataset.
Calculating a new column
Sometimes you wants to create derived data and attach them to the table in a new column. The number of reign years, for example can be calculated by subtracting the starting year from the ending year. The mid-year of the reign can be calculated by subtracting half of reign years from the ending year.
There are two ways of creating new columns: we can use the
$ operator or the mutate() function. We will
learn the latter in the next lesson, and focus on $
approach here.
First, you define an empty column and then use the assignment key
<- to write the product of the subtraction to it.
Investigate the result visually. Can you find out what kind of datatype the new column is?
# A tibble: 54 × 12
Name House Start…¹ End_y…² Birth…³ Death…⁴ Gender Dynasty Source Birth…⁵ Death…⁶
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 Gorm den G… Gorm 936 958 908 958 M Jellin… https… 908 958
2 Harald 1. … Gorm NA NA 936 985 M Jellin… https… 936 987
3 Toke_Gorms… Gorm 985 986 NA 986 M Jellin… https… <NA> <NA>
4 Svend 1. T… Gorm NA NA NA 1014 M Jellin… https… 17/04/… 03/02/…
5 Harald 2. Gorm 1014 1018 963 NA M Jellin… https… 994 1018
6 Knud 1. de… Gorm 1018 1035 NA 1035 M Jellin… https… 995 12/11/…
7 Hardeknud Gorm 1035 1042 995 1042 M Jellin… https… 1018 08/06/…
8 Magnus den… Fair… 1042 1047 1018 1047 M Jellin… https… 1024 25/10/…
9 Svend 2. E… Estr… 1047 1074 1024 1076 M <NA> https… 1019 28/04/…
10 Harald 3. … Estr… 1074 1080 1019 1080 M Jellin… https… 1041 17/04/…
# … with 44 more rows, 1 more variable: Reign_duration <dbl>, and abbreviated variable
# names ¹Start_year, ²End_year, ³Birth_year, ⁴Death_year, ⁵BirthDMY, ⁶DeathDMY [1] 22 NA 1 NA 4 17 7 5 27 6 6 9 8 30 3 9 11 25 20 39 9 2 7 27 33 7 3 3
[29] 35 12 9 43 8 33 31 10 10 23 29 60 22 29 31 16 20 42 31 9 15 43 6 35 25 51Now, create another column called Midyear by figuring
out what the middle year of each ruler’s reign is. This will be
important if we wish to plot the rulers on a graph’s x
axis.
Factors
R has a special data class, called factor, to deal with categorical data that you may encounter when creating plots or doing statistical analyses. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them.
Factors represent categorical data. They are stored as integers
associated with labels and they can be ordered (ordinal) or unordered
(nominal). Factors create a structured relation between the different
levels (values) of a categorical variable, such as days of the week or
responses to a question in a survey. This can make it easier to see how
one element relates to the other elements in a column. While factors
look (and often behave) like character vectors, they are actually
treated as integer vectors by R. So you need to be very
careful when treating them as strings.
Once created, factors can only contain a pre-defined set of values, known as levels. By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:
R will assign 1 to the level "cat" and
2 to the level "dog" (because c
comes before d, even though the first element in this
vector is "horse"). You can see this by using the function
levels() and you can find the number of levels using
nlevels():
[1] "cat" "dog" "horse"[1] 3Sometimes, the order of the factors does not matter. Other times you
might want to specify the order because it is meaningful (e.g., “low”,
“medium”, “high”). It may improve your visualization, or it may be
required by a particular type of analysis. Here, one way to reorder our
levels in the animals vector would be:
[1] horse cat cat dog
Levels: cat dog horse[1] horse cat cat <NA>
Levels: horse catIn R’s memory, these factors are represented by integers (1, 2), but
are more informative than integers because factors are self describing:
"horse", "cat" is more descriptive than
1, and 2. Which one is “cat”? You wouldn’t be
able to tell just from the integer data. Factors, on the other hand,
have this information built in. It is particularly helpful when there
are many levels. It also makes renaming levels easier. Let’s say we made
a mistake and need to recode “cat” to “Cat”. We can do this using the
fct_recode() function from the
forcats package (included in the
tidyverse) which provides some extra tools
to work with factors.
[1] "horse" "cat" animals <- fct_recode(animals, cat = "Cat")
## as an alternative, we could change the "cat" level directly using the
## levels() function, but we have to remember that "cat" is the second level
# levels(animals)[2] <- "Cat"
levels(animals)[1] "horse" "cat" [1] horse cat cat <NA>
Levels: horse catSo far, your factor is unordered, like a nominal variable. R does not
know the difference between a nominal and an ordinal variable. You make
your factor an ordered factor by using the ordered=TRUE
option inside your factor function. Note how the reported levels changed
from the unordered factor above to the ordered version below. Ordered
levels use the less than sign < to denote level
ranking.
animals_ordered <- factor(animals,
ordered = TRUE)
animals_ordered # after setting as ordered factor[1] horse cat cat <NA>
Levels: horse < catConverting factors
If you need to convert a factor to a character vector, you use
as.character(x).
[1] "horse" "cat" "cat" NA Converting factors where the levels appear as numbers (such as
concentration levels, or years) to a numeric vector is a little
trickier. The as.numeric() function returns the index
values of the factor, not its levels, so it will result in an entirely
new (and unwanted in this case) set of numbers. One method to avoid this
is to convert factors to characters, and then to numbers. Another method
is to use the levels() function. Compare:
year_fct <- factor(c(1990, 1983, 1977, 1998, 1990))
as.numeric(year_fct) # Wrong! And there is no warning...[1] 3 2 1 4 3[1] 1990 1983 1977 1998 1990[1] 1990 1983 1977 1998 1990Notice that in the recommended levels() approach, three
important steps occur:
- We obtain all the factor levels using
levels(year_fct) - We convert these levels to numeric values using
as.numeric(levels(year_fct)) - We then access these numeric values using the underlying integers of
the vector
year_fctinside the square brackets
Renaming factors
When your data is stored as a factor, you can use the
plot() function to get a quick glance at the number of
observations represented by each factor level. Let’s extract the
Gender column from our data frame, convert it into a
factor, and use it to look at the number of men and women in different
royal Genders:
## create a vector from the data frame column "Gender"
Gender <- kings$Gender
## convert it into a factor
Gender <- as.factor(Gender)
## let's see what it looks like
Gender [1] M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M F M M M M M M M M M M M M
[44] M M M M M M M M M M F
Levels: F M
Looking at the plot compared to the output of the vector, we can see that in addition to “no”s and “yes”s, there are some respondents for which the information about whether they were part of an irrigation association hasn’t been recorded, and encoded as missing data. They do not appear on the plot. Let’s encode them differently so they can counted and visualized in our plot.
## Let's recreate the vector from the data frame column "Gender"
Gender <- kings$Gender
## replace the missing data with "undetermined"
Gender[is.na(Gender)] <- "undetermined"
## convert it into a factor
Gender <- as.factor(Gender)
## let's see what it looks like
Gender [1] M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M F M M M M M M M M M M M M
[44] M M M M M M M M M M F
Levels: F M
Exercise 2
Rename the levels of the factor to full words with the first letter in uppercase: “Male”,“Undetermined”, and “Female”.
Now that we have renamed the factor level to “Undetermined”, can you create a barplot such that “Undetermined” is last (after “Female”)?
Formatting Dates
One of the most common issues that new (and experienced!) R users have is converting date and time information into a variable that is appropriate and usable during analyses. A best practice for dealing with date data is to ensure that each component of your date is available as a separate variable.
We are going to use the package
lubridate, which is included in the
tidyverse installation but not loaded by
default, so we have to load it explicitly with
library(lubridate).
Start by loading the required package:
The lubridate function ymd() takes a vector representing
year, month, and day, and converts it to a Date vector.
Date is a class of data recognized by R as being a date and
can be manipulated as such. The argument that the function requires is
flexible, but, as a best practice, is a character vector formatted as
“YYYY-MM-DD”.
Let’s say we have a vector of dates in character format:
chr [1:3] "7/31/2012" "8/9/2014" "4/30/2016"We can convert this vector to dates as :
[1] "2012-07-31" "2014-08-09" "2016-04-30"Argument format tells the function the order to parse
the characters and identify the month, day and year. The format above is
the equivalent of mm/dd/yyyy. A wrong format can lead to parsing errors
or incorrect results.
For example, observe what happens when we use a lower case y instead of upper case Y for the year.
[1] "2020-07-31" "2020-08-09" "2020-04-30"Here, the %y part of the format stands for a two-digit
year instead of a four-digit year, and this leads to parsing errors.
Or in the following example, observe what happens when the month and day elements of the format are switched.
[1] NA "2020-09-08" NA Since there is no month numbered 30 or 31, the first and third dates cannot be parsed.
We can also use functions ymd(), mdy() or
dmy() to convert character variables to date.
[1] "2012-07-31" "2014-08-09" "2016-04-30"Recap Exercises
Let us apply skills in this and previous lessons and make calculations on a column from the kings dataset.
- Using the kings dataset, eliminate the NAs from the years of reign.
[1] 22 NA 1 NA 4 17 7 5 27 6 6 9 8 30 3 9 11 25 20 39 9 2 7 27 33 7 3 3
[29] 35 12 9 43 8 33 31 10 10 23 29 60 22 29 31 16 20 42 31 9 15 43 6 35 25 51- Use the function
mean()to calculate what is the average reign duration.
- Use R to figure out which kings enjoyed more than average years of rule.
Keypoints
- Load external data from a .csv file into a data frame.
- Summarize the contents of a data frame.
- Subset values from data frames.
- Describe the difference between a factor and a string.
- Convert between strings and factors.
- Reorder and rename factors.
- Change how character strings are handled in a data frame.
- Examine and change date formats.
- Use
lubridatepackage to fix dates in R